From Internet to Intelligence: Tom Linton’s Vision for an AI-Driven Supply Chain

Oceanside Perspective
Jun 5
4 min read

AUTHOR | Anvesha Mishra

Executive Summary

At the UC San Diego Supply Chain Forum, Tom Linton, CEO of Linton Advisors and former Chief Procurement Officer at Flex and LG Electronics, shared a forward-looking view on how artificial intelligence (AI) is revolutionizing supply chain management. Drawing parallels to the internet boom, Linton argued that AI’s adoption is moving at a faster pace and will disrupt business operations in deeper, more operational ways. His talk centered around four key application areas—real-time data discovery, advanced scenario planning, interactive operations planning, and workforce adaptation. While highlighting strong early results, Linton also stressed the importance of human oversight and organizational readiness in ensuring AI’s responsible implementation.

AI’s Acceleration: Lessons from the Internet Era

Linton began by contextualizing AI’s rise through the lens of past technological shifts. “AI isn’t just about managing text—it’s about operational intelligence,” he said. In supply chains, the key variables are distance, time, and speed. They require rapid, accurate decisions. AI tools, he argued, shorten the latency of decision-making, reduce complexity, and empower companies to act with greater precision and speed than ever before.

Four Pillars of Transformation

Real-Time Data Discovery: AI can now track shifting consumer demand across regions and platforms in real time, improving forecasting and compliance. This capability helps companies minimize costly mismatches in inventory and contractual execution.
Scenario Planning & Digital Twins: Using AI-powered simulations, managers can answer critical “what-if” questions. Digital twins, virtual models of supply chain process help refine forecasts, manage disruptions, and optimize resources proactively.
Interactive S&OP: With AI-enabled tools, sales and operations planning becomes a continuous, real-time process. Insights no longer just explain the past; they anticipate the future.
Workforce Evolution: Successful AI adoption demands new training programs and redefined roles. “People must understand how to use these tools,” Linton emphasized, noting that AI augments rather than replaces human judgment.

Impact in Practice

Early adopters are already seeing tangible results. Companies report up to 15% improvements in inventory efficiency and a 2% rise in EBITA. AI has proven effective at streamlining decisions, cutting costs (SG&A and COGS), improving compliance, and eliminating human errors. Linton spotlighted broader opportunities from automating repetitive tasks to enhancing customer personalization and sustainability. In risk management, AI improves early detection of disruptions and recommends mitigation strategies. These developments allow organizations to be more agile, data-driven, and resilient in a fast-changing environment.

Challenges on the Horizon

Despite these gains, Linton urged caution. He noted real risks in intellectual property exposure, rising software costs, workforce dislocation, and decision-making opacity. Without strong governance and oversight, companies risk misapplying AI or losing control over its outcomes. “You can’t just let AI run freely,” he warned. Embedding AI into operations must be done thoughtfully, with clear frameworks for security, accountability, and human input.

The Road Ahead: AI Copilots and Autonomous Chains

Looking to the future, Linton envisioned supply chains where generative AI copilots assist in every function—procurement, logistics, planning—and where digital twins are continuously updated to reflect operational reality. By 2030, he projected over 86% of supply chains will be AI-transformed. But even in this autonomous future, his final message was clear: “Humans can innovate faster than AI can learn.”

Personal Reflections: From Data to Decisions

As a student at the Rady School of Management specializing in supply chain analytics, I found Tom Linton’s presentation both eye-opening and deeply aligned with the analytical journey I’ve been on throughout my graduate studies. One of the most compelling parts of his talk was the emphasis on digital twins and AI-enabled planning concepts we are actively applying in our capstone project with UC San Diego Health. Our team is using simulation modeling to tackle emergency department congestion, exploring how small operational tweaks, like adjusting triage logic or shifting discharge timing can relieve systemic bottlenecks. Linton’s discussion on predictive planning and intra-company process optimization gave me new language and frameworks to describe the value of this work.

At Rady, we frequently use regression models and Python-based workflows, but this talk helped me realize that simply building models isn’t enough. The real value lies in making timely, confident decisions with them. Moving forward, I see myself focusing more on how to integrate AI tools not just for analysis, but for continuous planning and operational improvement. This presentation has strengthened my conviction that mastering AI-powered planning systems is critical for anyone aspiring to lead in supply chain roles.

Conclusion

Tom Linton’s keynote provided a timely look at AI’s disruptive potential in supply chain management. While the technology offers significant gains, its success relies on thoughtful integration, skilled personnel, and responsible oversight. For future leaders, the takeaway is clear: embrace AI not as a magical solution, but as a powerful tool guided by human insight and strategic discipline.

What struck me most was Linton’s focus on the physical realities of the supply chain—distance, time, and speed—as the main challenges AI must tackle. AI can reduce distance with smarter network design and localized manufacturing, compress time through real-time analytics and faster decision-making, and enhance speed in logistics and overall planning. These are no longer abstract concepts; they are measurable factors that AI can help us manage with greater precision.

Authors' Bio

Anvesha Mishra

Master’s Student, Rady School of Management, UC San Diego

Anvesha Mishra is a master’s student at UC San Diego specializing in supply chain analytics and data-driven decision-making. With prior experience at Tech Mahindra in data analytics and visualization, she brings a practical, solution-oriented mindset to solving real-world operations problems.

Currently, Anvesha is working on a capstone project with UC San Diego Health, using simulation modeling to improve hospital efficiency and reduce emergency department congestion. A passionate guitarist, she finds creativity in both music and supply chain strategy—two worlds where timing, coordination, and flow are everything.